Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10795 publications
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AI coding assistants are rapidly becoming integral to modern software development. A key challenge in this space is the continual need to migrate and modernize codebases in response to evolving software ecosystems. Traditionally, such migrations have relied on rule-based systems and human intervention. With the advent of powerful large language models (LLMs), AI-driven agentic frameworks offer a promising alternative—but their effectiveness remains underexplored. In this paper, we introduce FreshBrew, a novel benchmark for evaluating AI-based agentic frameworks on project-level Java migrations. We benchmark several such frameworks, powered by state-of-the-art LLMs, and compare their performance against established rule-based tools. Our evaluation of AI agents on this benchmark of 228 repositories shows that the top-performing model, Gemini 2.5 Flash, can successfully migrate 56.5% of projects to JDK 17. Our empirical analysis reveals novel insights into the critical strengths and limitations of current agentic approaches, offering actionable insights into their real-world applicability. By releasing FreshBrew publicly upon acceptance, we aim to facilitate rigorous, reproducible evaluation and catalyze progress in AI-driven codebase modernization.
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For many practical applications of quantum computing, the slowest and most costly steps involve coherently accessing classical data. We help address this challenge by applying mass production techniques, which can sometimes allow us to perform operations many times in parallel for a cost that is comparable to a single execution[1-3]. We combine existing mass-production results with modern approaches for loading classical data using ``quantum read-only memory.'' We show that quantum mass production techniques offer no benefit when we consider a cost model that focuses purely on the number of non-Clifford gates. However, analyzing the constant factors in a more nuanced cost model, we find that it may be possible to obtain a reduction in cost of an order or magnitude or more for a variety reasonably-sized fault-tolerant quantum algorithms. We present several applications of quantum mass-production techniques beyond naive parallelization, including a strategy for reducing the cost of serial calls to the same data loading step.
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Junction and inductor models in ADS
arXiv (2025)
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This note is a follow up to Ref. [Naaman, IEEE TAS 2025], describing how to construct Josephson junction, inductor, and mutual inductance models using components that are available in the Keysight ADS core library.
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Bridging Fairness and Uncertainty: Theoretical Insights and Practical Strategies for Equalized Coverage in GNNs
Longfeng Wu
Yao Zhou
Jian Kang
Dawei Zhou
2025
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Graph Neural Networks (GNNs) have become indispensable tools in many domains, such as social network analysis, financial fraud detection, and drug discovery. Prior research primarily concentrated on improving prediction accuracy while overlooking how reliable the model predictions are. Conformal prediction on graphs emerges as a promising solution, offering statistically sound uncertainty estimates with a pre-defined coverage level. Despite the promising progress, existing works only focus on achieving model coverage guarantees without considering fairness in the coverage within different demographic groups. To bridge the gap between conformal prediction and fair coverage across different groups, we pose the fundamental question: Can fair GNNs enable the uncertainty estimates to be fairly applied across demographic groups? To answer this question, we provide a comprehensive analysis of the uncertainty estimation in fair GNNs employing various strategies. We prove theoretically that fair GNNs can enforce consistent uncertainty bounds across different demographic groups, thereby minimizing bias in uncertainty estimates. Furthermore, we conduct extensive experiments on five commonly used datasets across seven state-of-the-art fair GNN models to validate our theoretical findings. Additionally, based on the theoretical and empirical insights, we identify and analyze the key strategies from various fair GNN models that contribute to ensuring equalized uncertainty estimates. Our work estimates a solid foundation for future exploration of the practical implications and potential adjustments needed to enhance fairness in GNN applications across various domains.
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Circadian rhythm of heart rate and activity: a cross-sectional study
Maryam Khalid
Logan Schneider
Aravind Natarajan
Conor Heneghan
Karla Gleichauf
Chronobiology International (2025)
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ABSTRACT
Background: Circadian rhythms are commonly observed in a number of physiological processes. Consumer wearable devices have made it possible to obtain continuous time series data from a large number of individuals. We study circadian rhythms from measurements of heart rate, movement, and sleep, from a cohort of nearly 20,000 participants over the course of 30 days.
Methods: Participation was restricted to Fitbit users of age 21 years or older residing in the United States or Canada. Participants were enrolled through a recruitment banner shown on the Fitbit App. The advertisement was shown to 531,359 Fitbit users, and 23,239 enrolled in the program. Of these, we obtained heart rate data from 19,350 participants. We obtain the underlying circadian rhythm from time series heart rate by modeling the circadian rhythm as a sum over the first two Fourier harmonics. The first Fourier harmonic accounts for the 24-hour rhythmicity, while the second harmonic accounts for non-sinusoidal perturbations.
Findings: We observe a circadian rhythm in both heart rate and acceleration. From the diurnal modulation, we obtain the following circadian parameters: (i) amplitude of modulation, (ii) bathyphase, (iii) acrophase, (iv) non-sinusoidal fraction, and (v) fraction of day when the heart rate is greater than the mean. The amplitude, bathyphase, and acrophase depend on sex, and decrease with age. The waketime on average, follows the bathyphase by 2.4 hours. In most individuals, the circadian rhythm of heart rate lags the circadian rhythm of activity.
Interpretation: Circadian metrics for heart rate and activity can be reliably obtained from commercially available wearable devices. Distributions of circadian metrics can be valuable tools for individual-level interpretation.
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Differential privacy can be achieved in a distributed manner, where multiple parties add independent noise such that their sum protects the overall dataset with differential privacy. A common technique here is for each party to sample their noise from the decomposition of an infinitely divisible distribution. We introduce two novel mechanisms in this setting: 1) the generalized discrete Laplace (GDL) mechanism, whose distribution (which is closed under summation) follows from differences of i.i.d. negative binomial shares, and 2) The multi-scale discrete Laplace (MSDLap) mechanism, which follows the sum of multiple i.i.d. discrete Laplace shares at different scales. The mechanisms can be parameterized to have 𝑂(Δ^3𝑒^{−𝜀}) and 𝑂 (min(Δ^3𝑒^{−𝜀}, Δ^2𝑒^{−2𝜀/3})) MSE, respectively, where the latter bound matches known optimality results. Furthermore, the MSDLap mechanism has the optimal MSE including constants as 𝜀 → ∞. We also show a transformation from the discrete setting to the continuous setting, which allows us to transform both mechanisms to the continuous setting and thereby achieve the optimal 𝑂 (Δ^2𝑒^{−2𝜀/3}) MSE. To our knowledge, these are the first infinitely divisible additive noise mechanisms that achieve order-optimal MSE under pure differential privacy for either the discrete or continuous setting, so our work shows formally there is no separation in utility when query-independent noise adding mechanisms are restricted to infinitely divisible noise. For the continuous setting, our result improves upon Pagh and Stausholm’s Arete distribution which gives an MSE of 𝑂(Δ^2𝑒^{−𝜀/4}) [35]. We apply our results to improve a state of the art multi-message shuffle DP protocol from [3] in the high 𝜀 regime.
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Consensus or Conflict? Fine-Grained Evaluation of Conflicting Answers in Question-Answering
Eviatar Nachshoni
Arie Cattan
Shmuel Amar
Ori Shapira
Ido Dagan
2025
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Large Language Models (LLMs) have demonstrated strong performance in question answering (QA) tasks. However, Multi-Answer Question Answering (MAQA), where a question may have several valid answers, remains challenging. Traditional QA settings often assume consistency across evidences, but MAQA can involve conflicting answers. Constructing datasets that reflect such conflicts is costly and labor-intensive, while existing benchmarks often rely on synthetic data, restrict the task to yes/no questions, or apply unverified automated annotation. To advance research in this area, we extend the conflict-aware MAQA setting to require models not only to identify all valid answers, but also to detect specific conflicting answer pairs, if any. To support this task, we introduce a novel cost-effective methodology for leveraging fact-checking datasets to construct NATCONFQA, a new benchmark for realistic, conflict-aware MAQA, enriched with detailed conflict labels, for all answer pairs. We evaluate eight high-end LLMs on NATCONFQA, revealing their fragility in handling various types of conflicts and the flawed strategies they employ to resolve them.
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Deflating Deflationism: A Critical Perspective on Debunking Arguments Against AI Mentality
Geoff Keeling
Alex Grzankowski
Winnie Street
Henry Shevlin
Under Review, Minds and Machines (2025) (to appear)
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Abstract: Many people feel compelled to interpret, describe, and respond to Large Language Models (LLMs) as if they possess inner mental lives similar to our own. Responses to this phenomenon have varied. \textit{Inflationist} views endorse the truth of such ascriptions, granting that at least some attributions of mentality to LLMs are warranted. \textit{Deflationists} instead are more sceptical of these attributions, often cautioning against the risk that anthropomorphic projection may lead to misplaced trust or potentially even confusion about the moral status of LLMs. We advance this debate by assessing two common deflationary arguments against LLM mentality. What we term the \textit{robustness strategy} aims to undercut one justification for believing that LLMs are minded entities by showing that putatively cognitive and humanlike behaviours are not robust, failing to generalise appropriately. What we term the \textit{etiological strategy} undercuts attributions of mentality by challenging naive causal explanations of LLM behaviours, offering alternative causal accounts that weaken the case for mental state attributions. While both strategies offer powerful challenges to full-blown inflationism, we find that neither strategy provides a knock-down case against ascriptions of mentality to LLMs \textit{simpliciter}. With this in mind, we explore two modest forms of inflationism about LLM mentality that permit ascriptions of mentality to LLMs under certain conditions.\textit{ Practical modest inflationism }holds that we can, and perhaps should, mentalise LLMs where it is practical to do so, provided the benefits are weighed against relevant risks including the risk of potentially problematic forms of emotional dependency.\textit{ Metaphysical modest inflationism} holds that we can permissibly attribute those mental states and capacities which can be understood in metaphysically undemanding terms (such as knowledge and belief) while exercising greater caution when attributing more metaphysically demanding mental phenomena such as consciousness.
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Correspondance: Wearing a Fur Coat in the Summertime: Should Digital Pathology Redefine Medical Imaging?
Kenneth Philbrick
Brian Napora
John Groth
Mustafa Yousuf
Journal of Pathology Informatics (2025)
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In response to recent critiques, members of DICOM Working Group 26 assert that DICOM is the robust and essential standard for digital pathology, actively facilitating interoperability and communication of medical images far beyond simple pixel data. They highlight successful global deployments and collaborations (like the recent Connectathon) demonstrating DICOM's proven ability to integrate WSI scanners, archives, viewers, and AI tools. Despite concerns, DICOM offers flexible metadata encoding, robust security features, and strong industry and regulatory support, making it indispensable for patient care. The authors advocate for continued investment in and adoption of DICOM to advance efficiency, accuracy, and patient safety in integrated healthcare systems.
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Measuring software development can help drive impactful change. However, it’s a complex task, and getting started can be daunting as it involves understanding what you should measure, and determining what you can measure. This article provides a guide to selecting a framework that aligns with organizational measurement strategy.
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The Case for Leveraging Transport Signals to Improve Internet Speed Test Efficiency
Cristina Leon
Computer Communication Review (2025) (to appear)
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Internet speed tests are an important tool to enable consumers and regulators to monitor the quality of Internet access. However, increased Internet speeds to the home and an increased demand for speed testing pose scaling challenges to providers of speed tests, who must maintain costly infrastructure to keep up with this demand. In recent years, this has led the popular NDT speed test to limit data transfer to a total of 250MB, which comes at the cost of accuracy for high bandwidth speed test clients.
In this paper, we observe that the NDT speed test server’s congestion control algorithm (BBRv1) is also trying to estimate the capacity of the connection. We leverage this observation and signals from BBR to improve the accuracy and efficiency of speed tests. We first show how leveraging signals from BBR can more than double the accuracy of a 10MB test–from 17% to 43%–for clients with speeds over 400Mbps.
We then show how using BBR signals to adaptively end the speed test reduces data transfer by 36% and increased accuracy by 13% for high bandwidth clients, relative to a 100MB fixed length test. Even accounting for clients that never observe enough samples to utilize the BBR signal, this adaptive approach still uses 25% less data than a fixed 100MB test with 37-44% higher accuracy.
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We describe an efficient quantum algorithm for solving the linear matrix equation AX+XB=C, where A, B, and C are given complex matrices and X is unknown. This is known as the Sylvester equation, a fundamental equation with applications in control theory and physics. Our approach constructs the solution matrix X/x in a block-encoding, where x is a rescaling factor needed for normalization. This allows us to obtain certain properties of the entries of X exponentially faster than would be possible from preparing X as a quantum state. The query and gate complexities of the quantum circuit that implements this block-encoding are almost linear in a condition number that depends on A and B, and depend logarithmically in the dimension and inverse error. We show how our quantum circuits can solve BQP-complete problems efficiently, discuss potential applications and extensions of our approach, its connection to Riccati equation, and comment on open problems.
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Streaming Attention Approximation via Discrepancy Theory
Michael Kapralov
Insu Han
Ekaterina Kochetkova
Kshiteej Sheth
Amir Zandieh
2025
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Large language models (LLMs) have achieved impressive success, but their high memory requirements present challenges for long-context token generation. In this paper we study the streaming complexity of attention approximation, a key computational primitive underlying token generation.
Our main contribution is BalanceKV, a streaming algorithm for ϵ-approximating attention computations based on geometric process for selecting a balanced collection of Key and Value tokens as per Banaszczyk's vector balancing theory. We complement our algorithm with space lower bounds for streaming attention computation. Besides strong theoretical guarantees, BalanceKV exhibits empirically validated performance improvements over existing methods, both for attention approximation and end-to-end performance on various long context benchmarks.
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As the demand for data and digital services continues to escalate, data centers are evolving
into key players in the global energy consumption landscape. The necessity for sustainability
and energy efficiency in these facilities has led to the integration of Artificial Intelligence
(AI) technologies. This paper explores emerging AI trends that are shaping sustainable data
centers, focusing on optimization, predictive analytics, and machine learning applications,
along with their implications for operational efficiency and environmental impact. The rapid
growth of artificial intelligence (AI) has significantly impacted data center operations,
driving the need for sustainable practices. Emerging trends such as AI-driven energy
optimization, renewable energy integration, and advanced cooling technologies are
reshaping the industry. These innovations aim to reduce energy consumption, minimize
carbon footprints, and enhance operational efficiency. By leveraging AI, data centers can
predict maintenance needs, optimize energy usage, and adapt to real-time demands. This
paper explores the intersection of AI and sustainability, highlighting how these
advancements contribute to a more eco-friendly and efficient future for data centers.
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Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting
Zilong Wang
Steven Zheng
Swaroop Mishra
Yuwei Zhang
Anush Mattapalli
Ankur Taly
Jingbo Shang
ICLR 2025
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Retrieval augmented generation (RAG) has attracted a lot of attention across both academia and industry due to its capability in inserting timely and accurate evidence to the generation by large language models. However, the introduction of retrieved evidence largely makes the input prompt longer, which would harm the understanding quality of large language models and make it slower in actual usage scenarios. To solve these issues, we propose SpeculativeRAG, which leverages a smaller LLM to conduct the retrieval augmented generation for a larger LLM. The smaller LLM can digest a few pieces of evidence and generate multiple pieces of drafts in parallel rapidly, and these drafts will be verified by a large LLM to guarantee the quality. We achieve a higher speed as well as a better quality in the RAG results.
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